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Intuition as pattern recognition

Pattern recognition like intuition has a vague definition. We know what it means to recognize a face, but we cannot explain how we do it. Intuition is like that. An idea or solution to a problem pops into our mind, but we have no idea where it came from. Intuition is often perceived as an external force. A writer may speak of the characters in her novel as creating their own story.

We have recently developed two technologies for pattern recognition, neural nets and genetic algorithms, that do not use rational or deductive processes. The field of neural nets originated in a desire to better understand the human nervous system and to apply that understanding. There is no precise definition of a neural net, but they generally consist of a large number of simple processors connected to near neighbors. There are input and output connections and simple algorithms determine their relationship. The networks are trained through some process that adjusts the relationship between inputs and outputs to enhance some global result. In a simple example there are weights on each of the inputs. An output level is computed as the sum of the products of a weight for each input and signal level on that input. During a training period weights are increased on inputs with a strong signal when the system is getting closer to the desired response and decreased when it is moving away from it. Such simple devices can be extremely effective at solving problems for which there is no simple analytical solution. Of course they do not usually produce an optimal result.

Genetic algorithms, in a very simplified way, mimic biological evolution. A population of individuals with various traits is created. They are evaluated for fitness against some criteria. Those that rank highest have their properties mixed to create the next generation. A substantial portion of automated investing uses genetic algorithms[35, p. 87].

There is no simple way to explain why a neural net or genetic algorithm produces one response rather than another. One can do a detailed analysis of the state and explain exactly why this history and input produces this response, but that does not explain why one alternative is better than another. Because intuition is a generalized pattern recognition process, you cannot break up the result into a series of steps or analyze the process for mistakes. The way you discipline and develop intuition is different then the way you develop intellect.

Neural nets and genetic algorithms are increasingly important technologies that recognize patterns without a rational deductive process. We know in complete detail what neural nets and genetic algorithms do, but we do not understand how they work in the way we understand a rational process. These processes depend on a limited uniformity in the world yet they are robust in the face of anomalies. Our concern here is not the structure of these processes or the much harder problem of understanding the structure of human intuition. We want to look at the practical questions of how we develop intuition individually and culturally.

With intuition the search for patterns often includes archetypal material. Little in our lives is fundamentally original. Almost every situation we encounter is similar to an immense number of previous situations. These similarities are not limited to the human species. They go back through the history of evolution. For example walking past a dog that feels you are violating its territory raises instincts in the dog that are not so far removed from similar human instincts.

Many traditional approaches to developing intuition, like astrology, the I Ching and Tarot connect with archetypes. Evolution molds life to respond to recurring situations. The I Ching[53] is a catalog of recurring life patterns. It can strengthen our awareness and sensitivity to the archetypal patterns that intuition recognizes. With a better conscious understanding we know more about what to make of these patterns and we can better focus our intuition.


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